This paper examines racial and ethnic differences in high-cost mortgage lending in seven diverse metropolitan areas from 2004 to 2007. Controlling for credit score and other risk factors, AfricanAmerican and Hispanic borrowers are 103% and 78% more likely to receive high-cost mortgages for home purchases. Alarge part of the increase is attributable to sorting across lenders (55%-65%), and this, in turn, can be largely accounted for by the lender’s ex post foreclosure risk. The remaining within-lender differences are also concentrated in high-risk lenders, revealing the central role of these institutions in explaining market-wide racial and ethnic differences.

This paper develops a dynamic model of neighborhood choice along with a computationally light multi-step estimator. The proposed empirical framework captures observed and unobserved preference heterogeneity across households and locations in a flexible way. We estimate the model using a newly assembled data set that matches demographic information from mortgage applications to the universe of housing transactions in the San Francisco Bay Area from 1994 to 2004. The results provide the first estimates of the marginal willingness to pay for several non-marketed amenities?neighborhood air pollution, violent crime, and racial composition?in a dynamic framework. Comparing these estimates with those from a static version of the model highlights several important biases that arise when dynamic considerations are ignored.

We present new evidence on the evolution of black–white earnings differences among all men, including both workers and nonworkers. We study two measures: (i) the level earnings gap—the racial earnings difference at a given quantile; and (ii) the earnings rank gap—the difference between a black man's percentile in the black earnings distribution and the position he would hold in the white earnings distribution. After narrowing from 1940 to the mid-1970s, the median black–white level earnings gap has since grown as large as it was in 1950. At the same time, the median black man's relative position in the earnings distribution has remained essentially constant since 1940, so that the improvement then worsening of median relative earnings have come mainly from the stretching and narrowing of the overall earnings distribution. Black men at higher percentiles have experienced significant advances in relative earnings since 1940, due mainly to strong positional gains among those with college educations. Large relative schooling gains by blacks at the median and below have been more than counteracted by rising return to skill in the labor market, which has increasingly penalized remaining racial differences in schooling at the bottom of the distribution.

This article examines language patterns in U.S. television news coverage of police killings. First, we document that the media use syntactic structures—such as passive voice, nominalizations, and intransitive verbs—that obscure responsibility more often in cases of police killings than in cases of civilian killings. Through an online experiment, we demonstrate the significance of these syntactic differences, revealing that participants are less likely to hold police officers morally responsible and demand penalties when exposed to obfuscatory language, particularly in cases involving unarmed victims. Further analysis of news data shows greater use of obfuscatory language when the victims are unarmed or video footage is available—situations in which obfuscation may have the greatest impact. Exploring the causes of this differential obfuscation, we do not find evidence that it is driven by either demand-side factors or supply-side factors associated with TV station ownership and political leaning. Instead, our results suggest that narratives crafted by police departments are more likely drivers of media obfuscation. This article highlights how syntactic choices and their semantic consequences in media shape perceptions, extending beyond coverage volume and bias.

This paper develops a framework for estimating household preferences for school and neighborhood attributes in the presence of sorting. It embeds a boundary discontinuity design in a heterogeneous residential choice model, addressing the endogeneity of school and neighborhood characteristics. The model is estimated using restricted-access Census data from a large metropolitan area, yielding a number of new results. First, households are willing to pay less than 1 percent more in house prices-substantially lower than previous estimates-when the average performance of the local school increases by 5 percent. Second, much of the apparent willingness to pay for more educated and wealthier neighbors is explained by the correlation of these sociodemographic measures with unobserved neighborhood quality. Third, neighborhood race is not capitalized directly into housing prices; instead, the negative correlation of neighborhood percent black and housing prices is due entirely to the fact that blacks live in unobservably lower-quality neighborhoods. Finally, there is considerable heterogeneity in preferences for schools and neighbors, with households preferring to self-segregate on the basis of both race and education.

This paper examines racial and ethnic differences in high-cost mortgage lending in seven diverse metropolitan areas from 2004 to 2007. Controlling for credit score and other risk factors, African American and Hispanic borrowers are 103% and 78% more likely to receive high-cost mortgages for home purchases. A large part of the increase is attributable to sorting across lenders (55%-65%), and this, in turn, can be largely accounted for by the lender’s ex post foreclosure risk. The remaining within-lender differences are also concentrated in high-risk lenders, revealing the central role of these institutions in explaining market-wide racial and ethnic differences. Received December, 17 2014; editorial decision January 20, 2017 by Editor Philip Strahan.

Historical anecdotes abound of new investors being drawn into a booming asset market, only to suffer when the market turns. While the role of investor contagion in asset bubbles has been explored extensively in the theoretical literature, causal empirical evidence on the topic is much rarer. This paper studies the recent boom and bust in the US housing market and establishes that many novice investors entered the market as a direct result of observing investing activity of multiple forms in their own neighborhoods and that “infected” investors performed poorly relative to other investors along several dimensions.

For most people, buying a house is one of the most significant investment decisions of their lifetimes. Economists have mainly focused on the consumption aspects of this process. For example, a typical model in urban economics might frame the decision of where to live as a discrete choice over a bundle of housing and neighborhood attributes such as location, square footage, schooling options, and crime levels. The investment side of the problem has received considerably less attention, a surprising omission since housing assets comprise approximately two-thirds of the average American household’s financial portfolio, serve an important role in saving for retirement and, as has become increasingly apparent, can be quite risky. This paper views housing markets from an asset-pricing perspective, using finance theory to relate the risk premium of a housing asset (the difference between its expected return and the return for a risk-free investment) to its exposure to risk. As usual in finance, what matters for the risk premium of a housing asset is its exposure to systematic risk, not idiosyncratic risk. In our model, there are two forms of systematic risk to which housing assets are exposed: national risk (which is common to houses everywhere) and local risk (which affects all houses within a given metropolitan area, but nowhere else). Houses are said to be of the same type h if they are located in the same metropolitan area and have the same exposure to systematic risk. Our main conclusions are that (1) houses of every type face a common set of risk prices ( for the national risk and m for the local risk specific to metropolitan area m) that, together with appropriate measures of exposure to risk,

We use a novel research design to empirically detect the effect of social interactions on labor market outcomes. Using Census data on residential and employment locations, we examine whether individuals residing in the same city block are more likely to work together than those in nearby blocks. We find evidence of significant social interactions. The estimated referral effect is stronger when individuals are similar in sociodemographic characteristics. These findings are robust across specifications intended to address sorting and reverse causation. Further, the increased availability of neighborhood referrals has a significant impact on a wide range of labor market outcomes.

This paper analyzes the influence that juvenile offenders serving time in the same correctional facility have on each other's subsequent criminal behavior. The analysis is based on data on over 8,000 individuals serving time in 169 juvenile correctional facilities during a two-year period in Florida. These data provide a complete record of past crimes, facility assignments, and arrests and adjudications in the year following release for each individual. To control for the nonrandom assignment to facilities, we include facility and facility-by-prior-offense fixed effects, thereby estimating peer effects using only within-facility variation over time. We find strong evidence of peer effects for burglary, petty larceny, felony and misdemeanor drug offenses, aggravated assault, and felony sex offenses. The influence of peers primarily affects individuals who already have some experience in a particular crime category. We also find evidence that the predominant types of peer effects differ in residential versus nonresidential facilities; effects in the latter are consistent with network formation among youth serving time close to home.